Multi-touch interaction for interface prototyping

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Ambulatory and hospitalized patients with suspected and confirmed mpox: an observational cohort study from BrazilResearch in context

Summary: Background: By October 30, 2022, 76,871 cases of mpox were reported worldwide, with 20,614 cases in Latin America. This study reports characteristics of a case series of suspected and confirmed mpox cases at a referral infectious diseases center in Rio de Janeiro, Brazil. Methods: This was a single-center, prospective, observational cohort study that enrolled all patients with suspected mpox between June 12 and August 19, 2022. Mpox was confirmed by a PCR test. We compared characteristics of confirmed and non-confirmed cases, and among confirmed cases according to HIV status using distribution tests. Kernel estimation was used for exploratory spatial analysis. Findings: Of 342 individuals with suspected mpox, 208 (60.8%) were confirmed cases. Compared to non-confirmed cases, confirmed cases were more frequent among individuals aged 30–39 years, cisgender men (96.2% vs. 66.4%; p < 0.0001), reporting recent sexual intercourse (95.0% vs. 69.4%; p < 0.0001) and using PrEP (31.6% vs. 10.1%; p < 0.0001). HIV (53.2% vs. 20.2%; p < 0.0001), HCV (9.8% vs. 1.1%; p = 0.0046), syphilis (21.2% vs. 16.3%; p = 0.43) and other STIs (33.0% vs. 21.6%; p = 0.042) were more frequent among confirmed mpox cases. Confirmed cases presented more genital (77.3% vs. 39.8%; p < 0.0001) and anal lesions (33.1% vs. 11.5%; p < 0.0001), proctitis (37.1% vs. 13.3%; p < 0.0001) and systemic signs and symptoms (83.2% vs. 64.5%; p = 0.0003) than non-confirmed cases. Compared to confirmed mpox HIV-negative, HIV-positive individuals were older, had more HCV coinfection (15.2% vs. 3.7%; p = 0.011), anal lesions (45.7% vs. 20.5%; p < 0.001) and clinical features of proctitis (45.2% vs. 29.3%; p = 0.058). Interpretation: Mpox transmission in Rio de Janeiro, Brazil, rapidly evolved into a local epidemic, with sexual contact playing a crucial role in its dynamics and high rates of coinfections with other STI. Preventive measures must address stigma and social vulnerabilities. Funding: Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz (INI-Fiocruz)